NP4G: Network Programming for Generalization

In recent years, the development of Artificial Intelligence systems using neural network has been remarkable. However, this method has low explainability and is illogical. To solve this, there is an automatic programming method based on inductive inference. However, this method has also the problem of low versatility. In this study, we propose NP4G: Network Programming for Generalization, which can automatically generate programs by inductive inference. Because the proposed method can realize “sequence", “selection", and “iteration" in programming and can satisfy the conditions of the structured program theorem, it is expected that NP4G is a method that automatically acquires any programs by inductive inference. As an example, we automatically construct a bitwise NOT operation program from several training data by generalization using NP4G. Although NP4G only randomly selects and connects nodes, by adjusting the number of nodes and the number of phase of “Phased Learning", we show the bitwise NOT operation programs are acquired in a comparatively short time and at a rate of about 7 in 10 running. The source code of NP4G is available on GitHub as a public repository.